A new model order reduction method for the design of compensator by using moment matching algorithm

The aim of this paper is the construction of a new model reduction technique for large scale stable linear dynamic systems. It is principally focused on the dominant modes and time moments retention. This reduction implicates the translation of the overall important features confined in the large scale complete order model into the lower order system, allowing the computation of approximant denominator by using generalized pole clustering method. The approximant numerator is obtained by means of the factor division algorithm. As a result, a lower order system is obtained. To demonstrate its effectiveness, to highlight some fundamental of its features, and to accomplish its accuracy, a comparative study is done. Two standard numerical examples are taken, where approximant model computed by the proposed method is compared with the reduced order models computed from the recently proposed methods as well as well-known model reduction schemes. The paper is also emphasized on the design of compensator by using moment matching algorithm with the help of the reduced model. The design of compensator is validated and illustrated with the help of a standard numerical example taken from the literature.

Model order reduction using factor division algorithm and fuzzy c-means clustering technique

This paper proposes a novel hybrid approach that combines factor division algorithm and fuzzy c-means clustering technique for reducing the model order of high-order linear time invariant system. The process of clustering is used for finding the group of objects with similar nature that can be differentiated from the other dissimilar objects. The numerator of the higher order model is reduced using the factor division algorithm and the denominator of the higher order model is reduced using the fuzzy c-means clustering technique. The stability of the model is also verified using the pole zero stability analysis and it was found that the obtained reduced order model (ROM) is stable. Further, the steady state and transient response of the ROM is found to be better than the other existing techniques. The performance of the ROM is compared to other existing techniques in terms of integral square error, integral of time multiply squared error, integral absolute error and integral time-weighted absolute error.